Research Area:  Machine Learning

Abstract:

The rapid development of data and artificial intelligence technology has introduced new opportunities and challenges to aeronautical information intelligence. However, there are many obstacles in the sharing, reasoning and reusing aeronautical data due to the disunity of norms, the opacity of sharing and semantic ambiguity. To a large extent, as a basic method for processing, storing and deducing aeronautical data in the future, NER provides a new idea for the natural language processing of aeronautical information intelligence. In this paper, the problem with NER for aeronautical information is deeply analyzed, the relationship among the data model, the knowledge system and the named entity (NE) is combed, and the main characteristics of NE are summarized. At the same time, the resources that are useful to NER involving thematic databases, aviation domain ontology and evaluation indicators are described. Finally, two main directions of NER are suggested for further research, which is helpful in aviation development. This paper first provides a comprehensive survey of the approaches and directions of NER in a specific domain: aeronautical intelligence information.

Keywords:  
Named Entity Recognition
Aeronautical Information Intelligence
artificial intelligence
named entity (NE)
Deep Learning
Machine Learning

Author(s) Name:  Mi Baigang & Fan Yi

Publisher name:  Springer

DOI:  10.1007/s10462-022-10197-2

Volume Information:  

Paper Link:   https://link.springer.com/article/10.1007/s10462-022-10197-2